Analysis and address allocation of wireless building networks

ABSTRACT

A network node for a wireless building automation network, such as a lighting network, the node comprising means for measuring the signal quality or signal strength of the wireless connection to all wirelessly connected further nodes, storage means configured to store connection information e.g. based on the signal quality or signal strength together with an identifier for the associated wirelessly connected further node(s), and means for wirelessly forwarding the connection information to another node, wherein the network node is configured to receive at least one test pattern and/or to re-send the test pattern, the test pattern including the network node&#39;s identifier, e.g. the MAC address.

The invention relates to a system and method for assigning addresses,such as logical addresses or building addresses to network nodes forwireless networks, especially wireless building automation networks.

In particular, the invention is directed to the problem of mapping theidentity (address) of a wireless device to its physical position.

Wireless building automation networks in the sense of the invention arenetworks used to connect building technology devices forming the networknodes of the wireless network, for example lighting means (such aslamps), operating devices for lighting means, sensors (such as lightsensors, movement/motion sensors, acoustic sensors, optical sensors, . .. ) and actors (e.g. for controlling window blinds), and/or otherequipment (such as switches, interrupters, e.g. for controlling lights)and/or control units.

The invention is equally applicable to other wireless networks,including but not limited to wireless sensor networks (e.g. forstructure health monitoring), wireless industrial control networks,wireless computer networks or wireless telecommunication networks.Especially suited are those networks that require the location ofnetwork nodes to be known and where location addresses are (manually)assigned to network nodes.

The invention solves this problem by providing a system, method andnetwork node as set forth in the independent claims.

In one aspect, the invention provides a network node for a wirelessbuilding automation network, such as a lighting network, the nodecomprising means for computing connection information, i.e. the presenceand the distance of one or more neighboring network nodes, based on ameasurement of physical parameter of the wireless transmission channelbetween the neighboring nodes (e.g. the signal quality or signalstrength of the wireless connection to all wirelessly connected furthernodes), storage means configured to store said connection informationtogether with an identifier for the associated wirelessly connectedfurther node(s), and means for wirelessly forwarding the connectioninformation to another node. The network node is configured to receiveand/or send and/or echo at least one test pattern, the test patternincluding the network node's identifier, e.g. the MAC address.

The node can be a sensor such as e.g. a light, temperature, occupancy,smoke or movement sensor.

The node can be an operating device for lighting means, such as e.g.halogen, LED, OLED or gas discharge lamps.

The network node may store the connection information and nodeidentifiers in a neighbor table (a list of nodes the particular node isable to directly reach over the wireless channel).

The network node can store connection information to more than onenetwork node.

The network node may be configured to receive and store neighbor tablesof other network nodes and/or combine the received network nodes to agraph or sub-graph using a computing means.

The network node can provide a transmitting means to send the storedneighbor table(s), graph and/or sub-graph.

The measured physical parameter, e.g. on radio links, may be provided bya network node and can be combined by a network node with measuredconnection information provided by a second network node.

The pattern can e.g. be sent at, different levels of transmission power,in different sub-channels, using different encoding schemes and/orantenna configurations.

The network node can store the network node's identifier originatingfrom the test pattern. It can derive a channel characteristic for thecommunication channel the pattern was received on. Of all received testpatterns, the network node may compose the neighbor table.

The test pattern can either be a specially formed wireless packetoptimized for measuring a certain channel characteristic, or it can beregular network packet without special characteristics. The measuredchannel characteristic may also be derived from regular network traffic,the test pattern in this case being implicit.

The network node can at least be one of a sensor, lighting means,control device, operating device for lighting means, actor and buildingtechnology device.

In another aspect, the invention also provides a system having at leasttwo nodes as described above, as well as computing means designed forbuilding a measured connectivity graph based on the connectioninformation of said at least two nodes, and on the other hand producinga simulated connectivity graph based on a predefined known spatialarrangement of the at least two nodes as well as their buildingenvironment, and associating the identifier of the connectioninformation with a spatial position of each of the at least two nodes bymatching the measured connectivity graph and the simulated connectivitygraph.

The simulation can use a ray-tracing method to simulate connectivitybetween the nodes.

The two graphs may be at least partially matched. The measuredconnection information is provided as a neighbor table. The measuredconnection information, e.g. on radio-links, provided by a network nodecan be combined by a network node with measurement-based connectioninformation provided by a second network node.

The measurement may be triggered by a specific command sent to thenetwork nodes, wherein all network nodes activate respective receivingmeans.

In yet another aspect, the invention provides a method for automaticallyassigning spatial positions to network nodes of a building automationnetwork, such as a lighting network, the node, having at least two nodesas defined above, comprising the steps of building a measuredconnectivity graph based on the connection information of at least twonodes, producing a simulated connectivity graph based on a predefinedknown spatial arrangement of the at least two nodes as well as theirbuilding environment, and associating the identifier of the connectioninformation with a spatial position of each of the at least two nodes bymatching the measured connectivity graph and the simulated connectivitygraph.

Further aspects of the invention are now described in view of thefigures, wherein

FIG. 1 schematically shows a floor plan with network nodes installed(dots);

FIG. 2 shows a measured connectivity graph where the vertices (verticesare nodes of a graph) are the network nodes and the edges (thin line)result from measurements of physical parameters of communicationchannels, as performed by the network nodes. The vertices areexemplarily attributed with their identifiers;

FIG. 3 shows a first simulated connectivity graph where vertices are thenetwork nodes and the edges (thick line) result from a simulation ofnetwork node communication channels. The vertices are exemplarily beingattributed with the spatial position of their corresponding networknodes;

FIG. 4 shows a graph resulting from a successful matching of the firstand second graph resulting in a combination of node identifiers andspatial position for each vertex (of course a partial matching wouldalso possible as well as the occurrence of isolated network nodes in oneof the graphs).

In the inventive system, each network node that participates in thenetwork is preferably identifiable by an identifier, e.g. a distinctnetwork address, assigned to the network node at production stage,henceforth referred to as MAC address.

In a first step of the inventive method, each network node creates aneighbor table. This neighbor table for each network node contains thenetwork nodes that the network node can reach, i.e. the network node cancommunicate with. The neighbor table can also contain additionalinformation such as signal strength or signal quality of eachcommunication channel to another node in the neighbor table. Thecreation of neighbor tables taken alone is known from the prior art,e.g. from the ZigBee standard with mesh routing of the ZigBee Alliance(http://www.zigbee.org/).

Measuring the signal strength or signal quality (e.g. RSSI, ReceivedSignal Strength Indication) of neighboring nodes is just one example forgathering connection information, i.e. the presence and the distance ofone or more neighboring network nodes, based on the measurement of aphysical parameter of the wireless transmission channel between theneighboring nodes.

“Based on the measurement of a physical parameter” is to be understoodin the sense that the connection information can be identical to themeasured value, or be a value derived from the measured value.

Additionally or alternatively, other physical parameters may be used.E.g., wireless communication nodes can measure the time (“Time ofFlight”) a package, e.g. a data package and/or the test pattern, needsfor radio wave propagation. From this measured time the distance betweenthe nodes can be derived and used as distance measurement. This methodis especially useful for topology analysis of outdoor wireless networkswith wirelessly connected network nodes such as street lamps, trafficsigns, light modules, cameras, sensors, illuminated advertising andothers. In this scenario, the network nodes are typically separated bygreater distances as indoor network nodes. Using the Time of Flightinstead of e.g. RSSI (Received Signal Strength Indication) produces morereliable and more exact distances between the network nodes. This methodcan also be combined with the method described above, and e.g. onemethod can (signal quality/strength) be used for indoor analysis whilethe other method (Time of flight) is used for outdoor analysis.

In a second step, the neighbor tables of all network nodes arecollected, e.g. on a central node or a control unit. Based on theneighbor tables a graph is then created, in which the network nodes aregraph vertices and the edges in the graph represent possiblecommunication channels between a network node and its neighbors inalignment with the information derived from the neighbor table for eachdevice. Additional information such as signal strength or signal qualityis represented as attributes or as weights for the graph edges. Hence,the generated graph represents the relative positions of the networknodes to each other on basis of communication channel measurements eachnetwork node performed to generate the neighbor table. In this graph,each network node is identified with an identifier, e.g. a uniqueaddress such as a MAC address. In one aspect of the invention, the nodesin the neighbor tables are also identified with their respectiveidentifier.

Next, a second graph is generated based on, e.g., abuilding/installation plan (construction plan) of a building, in whichthe position of the installed network nodes is marked (at least thenodes that should be installed according to the plan). While thevertices of the second graph are easily derivable from the plan used,connecting these vertices by edges is performed by taking into accountfeatures of the plan such as thickness of walls and ceilings, materialused in the building and other factors that can be derived from the planused.

Based on this information, the communication channels possible betweenthe installed nodes are simulated or calculated and the edge-weights arethe calculated signal strength or signal quality of the communicationchannel.

For simulating the communication channels between the network nodes,e.g. ray tracing can be used. Using ray tracing, the node communicationchannels of the installed nodes can be simulated or calculated byplacing the eye-point (camera point) in one network node (that is at theposition of the installed network node) and putting the light source inanother installed network node (that is at the position of anotherinstalled network node).

Taking into account the information of the used plan, e.g. ceilings,walls, material used in the building, the communication channels can beestablished by tracing the way from the eye-point to the light or viceversa. The communication channels between a vertex and its simulatedneighbors, i.e. the network nodes a network node can connect to, cane.g. also be stored in a table. By this, the communication channelssimulated in the second graph also take into account physical conditionsof the building, such as multipath propagation caused by reflectionsand/or absorptions by walls and ceilings. The plan used herein can ofcourse be a 3D-plan, e.g. a CAD-plan.

Based on the simulation, and taking into account the spatial positions,the second graph is assembled directly. The simulation is performed oncomputer hardware and may be sped-up by use (special) GPUs (GraphicsProcessing Unit). As a result, the vertices of the second graph areattributed by positioning or location information.

It is now the goal of the invention to match the two graphs, in order toassign network identifiers of the first graph to the node positionsderived from the plan in the second graph. Hence the invention aims atautomatically assigning the logical addresses or positions to thehardware identifiers of the network nodes.

In the next step, both graphs are therefore matched to each other tofind the most promising and most probable alignment of the productionaddresses to the absolute or relative coordinate derived from the planused.

For example, in a newly installed wireless network system the networknodes can be put in a mode in which they perform measurements to filltheir neighbor tables and to obtain parameters such as RSSI (ReceivedSignal Strength Indicators) and/or LQI (Link Quality Indicator) whichare also stored in the neighbor table for each neighbor. The neighbortable can also contain more than one entry to a particular neighbor as ameans to account for multipath propagation due to reflections or similarphenomena resulting from the features of the building. Similarly, thesecond graph can also contain more than one edge between nodes as aresult of the simulation taking multipath propagation into account.

E.g. the equalizer built into GSM modules, which is amongst other thingsresponsible for cancellation of the echo resulting from multipathpropagation, is able to provide measurement data on the multipathpropagation characteristics of the channel. The workings of theequalizer can also be included into the simulation such as to laterincorporate multipath propagation characteristics into graph matching.

The neighbor tables resulting from these measurements can then becollected in a central point and might, e.g. by being transmittedwirelessly to this central point.

A neighbor table already represents a small sub-graph of network as seenfrom only one particular network node. It is possible to join sub-graphstogether to build ever larger sub-graphs, representing ever largerportions of the overall network. Therefore, a hierarchy can beestablished where at the lowest hierarchy level at least the neighbortables (sub-graphs) of two network nodes are joined, where the sub-graphis submitted to a next level, which then joins the received sub-graphwith another sub-graph and so on until all neighbor tables are joined.This join-operation can also be performed by the network nodes, at theperiphery of the network. A network node can be designated to output thefully joined graph.

In one aspect of the invention it is possible that the matching of thefirst and the second graph is not unambiguously defined or is notpossible at all. In this case, information is provided to a human userincluding the information about the nodes, the network nodes, for whichthe fixed addresses of the network node could not be matched to alogical address, e.g. the position of the device in the building. Withthis information, the human user can complete or perform the matching.Test runs of the algorithm show that only a few network nodes remainunmatched and therefore, the algorithm significantly supports a humanuser by automatically matching logical addresses to fixed network nodeaddresses.

A detailed and exemplary description of the invention is now provided.

For solving the above problems, the inventive system and method hencefeatures three components:

-   1. Channel estimation: A mechanism to produce a network-wide    estimation of the wireless connectivity based on geographical    layout, floor plan and deployment plan of the wireless network.-   2. Channel measurement: A mechanism to gather actual connectivity    information and device identities from the deployed wireless    network.-   3. Matching algorithm: A mechanism to match estimated and actual    channel characteristic to produce a mapping between wireless device    identities and their physical positions as charted in the deployment    plan.

In order to commission a wireless network, the identities and positionsof the network nodes need to be known. In the past there have been twoapproaches of solving this problem: Assigning identities (addresses)during installation or during commissioning of the wireless networkusing some out-of-band mechanism; and registering previously assigned orrandomly assigned identities during installation or commissioning usingsome out-of-band mechanism.

The invention now solves the addressing problem using “in-band”mechanisms, thereby eliminating one otherwise manually performed stepfrom the installation and start-up procedure.

The necessary building, floor and/or deployment plans most of the timeare already available as they need to be produced for installationpurposes.

To the addressing problem, there exists an affiliated problem known as“wireless location estimation”. The key difference to the addressingproblem is that wireless location estimation aims at producing aposition expressed in coordinates (meters). It maps information onconnections to positions. Wireless topology analysis, on the other hand,rearranges previously known positions according to connectioninformation so as to map addresses to positions.

The channel estimate, the second graph, is a graph where the installednetwork nodes are the vertices and the radio links between the devicesare the edges.

The vertices are attributed by the device types (manufacturer/typedesignation) and the device positions. The edges are assigned weightswhich indicate some quality of the wireless channel, mostly the receivedsignal strength or time of flight. The graph may be fully connected, orthe edges where the weight is smaller than some cut-off-value orthreshold may be removed during graph generation.

The crucial problem is how to calculate the weights of the second(simulated) graph. Using the distance between node positions is not agood enough estimate. Therefore, additional information derived from theplan (walls, floors, ceilings, their thickness and material, position ofdoors and windows) is used in a ray tracing algorithm that for each pairof devices calculates an estimate for the signal strength by factoringin signal reflection and transmission through obstacles. Additionally,factors such as mounting orientation of network nodes, antennacharacteristics, RF reflecting or absorbing materials such as metalpanels in dropped ceilings, concrete reinforcements, RF propagation viaoutside space, lift shafts, fire doors, etc can be accounted for in themodel.

The channel measurement, the first graph, has the same basic form as thechannel estimate (graph, vertices, attributes, edges, weights) but ismeasured using dedicated network functionality: The wireless network isset to a “channel measurement mode” during which normal operation isinterrupted. The procedure is as follows:

-   -   The procedure is introduced by a command broadcast from a        management device to all network nodes of the network.    -   All network nodes permanently activate their receivers, even        network nodes that would normally keep their receiver        deactivated most of the time.    -   A special test pattern is propagated throughout the network        using a similar strategy as broadcasting. All devices need to        re-broadcast the pattern. Extra care can be taken to avoid or        mitigate the chances of collisions. The test pattern includes        each sending network node's preconfigured identifier, e.g. the        MAC address. The test pattern may spawn several messages,        possibly sent at different levels of transmission power, in        different sub-channels, using different encoding schemes or        antenna configurations.    -   Network nodes receiving the test pattern save the originator's        identifier and derive a channel characteristic, for example the        received signal strength. Out of all received test patterns, the        devices compose a neighbor table containing identifiers and        connectivity information. Due to memory constraints, the        neighbor table may be shortened to only include the neighbors        which were received most strongly.    -   Network nodes can then return to normal behavior with respect to        power cycling the receiver.    -   Finally the devices transmit their own identifier (MAC level        address, manufacturer, type designation, . . . ) and the        neighbor table back to the management device.

At that point the channel measurement graph can be constructed byassembling the received identities and neighbor tables into a weighted,attributed graph, the first graph. Each edge and weight is composed of amaximum of two neighbor table entries (each of the adjacent verticesreceiving the test pattern form the other). Due to channel asymmetry,receiver and transmitter differences and time variability of thechannel, the two received signal strengths corresponding to one edge ofthe graph may differ or one of them may be absent. Big deviations may bea sign of test pattern collisions or interference. Also the abovechannel measurement procedure may be repeated or for the affected subgraph.

The two graphs resulting from channel estimation and channel measurementare substantially similar. They differ in the vertices of channelestimation being attributed with a location or position, e.g., aposition in a building, and the vertices of the channel measurementbeing attributed with the identifiers. Other than the attributes, thegraphs also differ with respect to simulation and measurement errors,and most importantly, they are thoroughly permuted with respect to eachother.

If the vertices that correspond to each other in the two graphs arefound, this results in the desired mapping from identifier to position.While the two graphs are topologically similar, they are by no means thesame.

The numeric values from estimation and measurement are profoundlydivergent, and also small topological differences may need to betolerated for (wrongly installed or defective network nodes show updifferently or not at all in the measurement).

The problem of matching two topologically similar, weighted andoptionally attributed graphs against each other is called the weightedgraph matching problem. There is an amount of academic research aboutit, mainly in the field of pattern recognition.

Finding the optimum match is hard, since its complexity grows with thefaculty of the graph size. There exist however several heuristicapproaches, cited below.

The heuristic is based on a 1996 paper by Steve Gold and AnandRangarajan “A Graduated Assignment Algorithm for Graph Matching”. Thealgorithm seeks to find a permutation matrix M that encodes the soughtafter mapping between the first graph and the second graph. M is foundby minimizing the following objective function E_(wg)(M):

${E_{wg}(M)} = {{- \frac{1}{2}}{\sum\limits_{a = 1}^{A}\; {\sum\limits_{i = 1}^{I}\; {\sum\limits_{b = 1}^{A}\; {\sum\limits_{j = 1}^{I}\; {M_{ai}M_{bj}C_{aibj}}}}}}}$

A and I are the sizes of the adjacency matrices G1 and G2 representingthe two graphs. C_(aibj) is distance measure between edges of the graphsand compares all edges of G1 to all edges of G2. It is defined by:

$C_{aibj} = \left\{ \begin{matrix}0 & {{if}\mspace{14mu} {either}\mspace{14mu} G_{1,{ab}}\mspace{14mu} {or}\mspace{14mu} G_{2,{jj}}\mspace{11mu} {is}\mspace{14mu} {NULL}} \\{1 - {c{{G_{1,{ab}} - G_{2,{ij}}}}}} & {otherwise}\end{matrix} \right.$

c is a normalization constant that normalizes the expected edge-distancein C_(aibj) to be zero-mean.

The product M_(ai)M_(bj) in the objective function E_(wg)(M) selectsjust the right edge-distances from C_(aibj) such that the objectivebecomes minimal if M represents the right permutation.

Because of the discrete nature of the optimization problem (M is apermutation matrix and contains only zeros and ones), it is hard tosolve directly. Instead, the algorithm follows a graduated optimizationstrategy starting with a relaxed version of the problem that iterativelysolves the optimization problem.

Optimization step: Solve the optimization for a doubly stochastic matrixM instead of a permutation matrix. A doubly stochastic matrix can bethought of a continuous equivalent of the discrete permutation matrix.The optimization step is performed by calculating the partial derivativeQ≈∂E_(wg)/∂m and then applying Sinkhorn's algorithm to find a doublystochastic result.

Soft max step: Prior to Sinkhorn's algorithm the partial derivative isscaled by a soft-max: M′_(ai)=exp(βQ_(ai)). β starts small and isincreased at every iteration to push the doubly stochastic matrix toapproach a permutation matrix.

The algorithm works as exemplarily shown below in a pseudo-codenotation;

Initialize β to β₀, M_(ai) to 1 Do until β > β_(f) Do until M convergesQ_(ai) ← −∂E_(wg)/∂M M′_(ai) ← exp(βQ_(ai)) Do until M′ converges Updaterows of M′: M′_(ai) ← M′_(ai) / Σ_(I) M′_(ai) Update cols of M′: M′_(ai)← M′_(ai) / Σ_(A) M′_(ai) M ← M′ β ← β_(r)ββ, β_(r), β₀, β_(f) are the control parameter, its rate of increase, itsstart and end value. The most computationally expensive step iscalculating the partial derivative. It is implemented to run on the GPU(a powerful graphics co-processor) and uses sparse matrix representationfor the adjacency matrices G1 and G2.

The complexity of the algorithm is proportional to the square of thecombined number of edges in both graphs and in the currentimplementation can match graphs up to 2000 vertices and 40000 edges in amatter of minutes on current computer hardware.

If the method laid out above cannot find a good match, possibly due totoo much symmetry in the geometry of the building, or due to badestimates, the matching mechanism can ask the operator to give itadditional fixed points. It calculates the location with the biggesttopological ambiguity and asks the operator to walk there, and manually,using some out-of-band method, uncover the address of a particulardevice occupying a particular position. With that additional informationthe matching method is run again until a solution with high enoughconfidence is found.

1. A network node for a wireless building automation network, the nodecomprising: means for measuring a physical parameter of the wirelessconnection to all wirelessly connected further nodes, storage meansconfigured to store connection information based on the measuredphysical parameter together with an identifier for the associatedwirelessly connected further node(s), means for wirelessly forwardingthe connection information to another node, and means for sending and/orreceiving and/or echoing at least one test pattern, the test patternincluding the network node's identifier.
 2. The network node of claim 1,wherein the network node stores the measured connection information andnode identifiers in a neighbor table.
 3. The network node of claim 1,wherein the network node stores connection information to more than onenetwork node.
 4. The network node of claim 1, wherein the network nodeis configured to receive and store neighbor tables of other networknodes and/or combine the received network tables to a graph or sub-graphusing a computing means.
 5. The network node of claim 4, wherein themeans for wirelessly forwarding is configured to wirelessly send thestored neighbor table(s), graph and/or sub-graph.
 6. The network node ofclaim 1, wherein the measured connection information provided by anetwork node is combined by a network node with measured connectioninformation provided by a second network node.
 7. The network node ofclaim 1, wherein the pattern is sent: at different levels oftransmission power, or in different sub-channels, or using differentencoding schemes and/or antenna configurations.
 8. The network node ofclaim 1, wherein the pattern is used to measure time of flight.
 9. Thenetwork node of claim 1, wherein the network node stores the networknode's identifier originating the test pattern and derives a channelcharacteristic for the communication channel the pattern was receivedon, and wherein, of all received test patterns, the network nodecomposes the neighbor table.
 10. The network node of claim 1, whereinthe network node is at least one of a sensor, lighting means, controldevice, operating device for lighting means, actuator and buildingtechnology device.
 11. A system having at least two wirelesslycommunicating nodes, as well as computing means configured for buildinga measured connectivity graph based on the connection information of atleast two nodes, producing a simulated connectivity graph based on apredefined known spatial arrangement of the at least two nodes as wellas their building environment, and associating the identifier of theconnection information with a spatial position of each of the at leasttwo nodes by matching the measured connectivity graph and the simulatedconnectivity graph.
 12. The system of claim 11, wherein the wirelesslycommunicating nodes are nodes comprising means for measuring a physicalparameter of the wireless connection to all wirelessly connected furthernodes, storage means configured to store connection information based onthe measured physical parameter together with an identifier for theassociated wirelessly connected further node(s), means for wirelesslyforwarding the connection information to another node, and means forsending and/or receiving and/or echoing at least one test pattern, thetest pattern including the network node's identifier.
 13. The system ofclaim 11, wherein the simulation uses a ray-tracing method to simulateconnectivity between the nodes.
 14. The system of claim 11, wherein thetwo graphs are at least partially matched.
 15. The system of claim 12,wherein the measured connection information is provided as a neighbortable.
 16. The system of claim 11, wherein measured connectioninformation provided by a network node is combined by a network nodewith measured connection information provided by a second network node.17. The system of claim 16, wherein the measurement is triggered by aspecific command sent to the network nodes, wherein all network nodesactivate respective receiving means.
 18. The system of claim 16, whereina test pattern is propagated to the network nodes, wherein all networknodes re-send the pattern, wherein the test pattern includes eachsending network node's identifier and wherein the pattern messages aresent: at different levels of transmission power, or in differentsub-channels, or using different encoding schemes and/or antennaconfigurations.
 19. The system of claim 18, wherein the test pattern isused to measure time of flight.
 20. The system of claim 18, wherein thenetwork nodes receiving the test pattern store the originating networknode's identifier and derive a channel characteristic for thecommunication channel the pattern was received on, and wherein, of allreceived test patterns, the network nodes compose the neighbor tables.21. A method for automatically assigning spatial positions to networknodes of a building automation network having at least two wirelesslycommunicating nodes, comprising the steps of building a measuredconnectivity graph based on connection information of at least twonodes, producing a simulated connectivity graph based on a predefinedknown spatial arrangement of the at least two nodes as well as theirbuilding environment, and associating an identifier of the connectioninformation with a spatial position of each of the at least two nodes bymatching the measured connectivity graph and the simulated connectivitygraph.
 22. The method of claim 21, wherein the wirelessly communicatingnodes are nodes comprising means for measuring a physical parameter ofthe wireless connection to all wirelessly connected further nodes,storage means configured to store connection information based on themeasured physical parameter together with an identifier for theassociated wirelessly connected further node(s), means for wirelesslyforwarding the connection information to another node, and means forsending and/or receiving and/or echoing at least one test pattern, thetest pattern including the network node's identifier.
 23. The method ofclaim 21, wherein the simulation uses a ray-tracing method to simulatethe communication channels to the nodes.
 24. The method of claim 21,wherein the two graphs are at least partially matched.
 25. The method ofclaim 22, wherein the measured connection information is provided as aneighbor table.
 26. The method of claim 22 wherein measured connectioninformation provided by a network node is combined by a network nodewith measured connection information provided by a second network node.27. The method of claim 21, wherein the measurement is triggered by aspecific command sent to the network nodes, wherein all network nodesactivate respective receiving means.
 28. The method of claim 21, whereina test pattern is propagated to the network nodes, wherein all networknodes re-send the pattern, wherein the test pattern includes eachsending network node's identifier and wherein the pattern messages aresent: at different levels of transmission power, or in differentsub-channels, or using different encoding schemes and/or antennaconfigurations.
 29. The method of claim 28, wherein the network nodesreceiving the test pattern store the originating network node'sidentifier and derive a channel characteristic for the communicationchannel the pattern was received on, and wherein, of all received testpatterns, the network nodes compose the neighbor tables.